Syllabus

Extended Syllabus PDF

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Book

PDF - (Pg. 127-133 and 150-155)

R Language - Part 1 & Part 2

• Basic Math, Assigment, Comment
• Data Types - Classes
  • Numeric
  • Integer
  • Logical
  • Character
• Data Structures - Objects
  • Vector
  • Matrice
  • Array
  • Data Frame
  • List
• Special Values, Attributes

R Language - Part 3

• Read

• Write

• Plot

Getting Started

• Assignment; <-
• Comment; #
• Help; ?func .or. help(func)
• Install Packages; install.packages()
• Call from Library; library()
• Basic Math;
  • addition; +
  • subtraction; -
  • multiplication; *
  • division; /
  • exponentiation; ^
  • the square root; sqrt

Basic Math

Basic Math

a <- 2.3
(6*a+42)/(3^(4.2-3.62))

## [1] 29.50556

isTRUE((6*a+42)/(3^(4.2-3.62))==29.50556)

## [1] FALSE

Scientific Math

Problem: Compute double, triple or higher order integrals

Scientific Math

Problem: Compute double, triple or higher order integrals

Scientific Math

Problem: Compute double, triple or higher order integrals

# install.packages("cubature")
library(cubature)

cube_f <- function(x) 1
adaptIntegrate(cube_f,lowerLimit = c(0,0,0),upperLimit = c(4,4,4))

## $integral
## [1] 64
## 
## $error
## [1] 7.105427e-15
## 
## $functionEvaluations
## [1] 33
## 
## $returnCode
## [1] 0

Data Types - Classes

• Numeric

# Any number with (or without) a decimal point.
a <- 3

• Integer

# Sub-class of the numeric class. The suffix L tells R to store.
a <- 3L

• Logical

# TRUE or FALSE - Logical Operators. < , > , == , >= , <= , != ... 
a <- 3<2

• Character

# Data type consists of letters or words. String. with quotes: " … "
a <- "3"

is.XXX() and class()

Data Types - Classes

name1 <- emir
name1 <- "emir"
name2 <- name1
name3 <- "name1"

number1 <- 32
number2 <- "32"
number3 <- 1:10
number4 <- seq(1,10)

var1 <- TRUE
var2 <- "TRUE"

answer1 <- is.logical(var1)
answer2 <- var1 + answer1 / 3

surname1 <- "toker"
print(name1)
print(surname1)

print(name1,surname1)

is.XXX() and class()

Data Structures - (Atomic) Vector

name <- "emir"
surname <- "toker"

print(c(name,surname))     # c means “combine”

Vector : The simplest data structure in R

name <- "emir"
surname <- "toker"
name_surname <- c(name,surname)
length(name_surname)

print(c("21","21"))   
print(c("21",21))  
print(c(21,21))   

Data Structures - (Atomic) Vector

spring_months <- c("March", "April","May","June")

spring_months

length(spring_months)

dim(spring_months)

spring_months[1]

spring_months[3:4]

str(spring_months) # Structure

substr(spring_months, start = 1, stop = 3)  # Substrings

strsplit(spring_months,"")

gsub("a", "A", spring_months)   # Matching and Replacement

?str , ?substr , ?strsplit , ?gsub

Data Structures - Matrice

Vectors indexed using two indices instead of one.

[ row, col ]

a <- c(1:3)
# str(a) and dim(a) and length(a)
b <- matrix(1:3, nrow = 1, ncol = 3)
# str(b) and dim(b) and length(b)

Data Structures - Matrix

a <- c(1:3)
b <- matrix(1:3, nrow = 1, ncol = 3)

## [1] 1 2 3

##      [,1] [,2] [,3]
## [1,]    1    2    3

c <- matrix(1:9, nrow = 3, ncol = 3)

##      [,1] [,2] [,3]
## [1,]    1    4    7
## [2,]    2    5    8
## [3,]    3    6    9

d <- matrix(1:9, nrow = 3, ncol = 3, byrow = TRUE)

##      [,1] [,2] [,3]
## [1,]    1    2    3
## [2,]    4    5    6
## [3,]    7    8    9

Data Structures - Matrix

my_mat <- matrix(runif(n=20, min=0, max=100), nrow = 4,  ncol = 5)

##          [,1]     [,2]      [,3]      [,4]      [,5]
## [1,] 47.20439 12.63902 95.299868 56.496099 69.478405
## [2,] 38.58236 54.94485 24.343080  1.429381 67.859400
## [3,] 64.92683 86.18240  6.103165 85.902938  1.749736
## [4,] 96.42536 39.27326 84.330873 29.567810 95.258230

add <-  matrix(seq(from=10, to=60, by=10), nrow = 2, ncol = 3)

##      [,1] [,2] [,3]
## [1,]   10   30   50
## [2,]   20   40   60

my_mat[2:3,2:4] <- add

##          [,1]     [,2]     [,3]     [,4]      [,5]
## [1,] 47.20439 12.63902 95.29987 56.49610 69.478405
## [2,] 38.58236 10.00000 30.00000 50.00000 67.859400
## [3,] 64.92683 20.00000 40.00000 60.00000  1.749736
## [4,] 96.42536 39.27326 84.33087 29.56781 95.258230

Data Structures - Array

str(arr)

##  int [1:4, 1:3, 1:2] 1 2 3 4 5 6 7 8 9 10 ...

# dim(arr)
# length(arr)

x <- 1:24

##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
## [24] 24

arr <- array(x, dim = c(4,3,2)) #raw,col,level

## , , 1
## 
##      [,1] [,2] [,3]
## [1,]    1    5    9
## [2,]    2    6   10
## [3,]    3    7   11
## [4,]    4    8   12
## 
## , , 2
## 
##      [,1] [,2] [,3]
## [1,]   13   17   21
## [2,]   14   18   22
## [3,]   15   19   23
## [4,]   16   20   24

Data Structures - Array

[ row, col, level ]

Data Structures - Array

arr <- array(data=10:30,dim=c(2,5,2))

## , , 1
## 
##      [,1] [,2] [,3] [,4] [,5]
## [1,]   10   12   14   16   18
## [2,]   11   13   15   17   19
## 
## , , 2
## 
##      [,1] [,2] [,3] [,4] [,5]
## [1,]   20   22   24   26   28
## [2,]   21   23   25   27   29

arr[2,2:4,1:2]

##      [,1] [,2]
## [1,]   13   23
## [2,]   15   25
## [3,]   17   27

arr[1,1:5,2]

## [1] 20 22 24 26 28

Data Structures - Array

array <- array(data=seq(2,144,2),dim=c(3,6,4))

## , , 1
## 
##      [,1] [,2] [,3] [,4] [,5] [,6]
## [1,]    2    8   14   20   26   32
## [2,]    4   10   16   22   28   34
## [3,]    6   12   18   24   30   36
## 
## , , 2
## 
##      [,1] [,2] [,3] [,4] [,5] [,6]
## [1,]   38   44   50   56   62   68
## [2,]   40   46   52   58   64   70
## [3,]   42   48   54   60   66   72
## 
## , , 3
## 
##      [,1] [,2] [,3] [,4] [,5] [,6]
## [1,]   74   80   86   92   98  104
## [2,]   76   82   88   94  100  106
## [3,]   78   84   90   96  102  108
## 
## , , 4
## 
##      [,1] [,2] [,3] [,4] [,5] [,6]
## [1,]  110  116  122  128  134  140
## [2,]  112  118  124  130  136  142
## [3,]  114  120  126  132  138  144

array[1,1,1:4]
array[1,1,]
array[1,1,4:1]
array[1,1,c(1:4!=2)]

array[1,1,2]
array[1,1,which(x==2)]
array[1,1,which(x<=2)]

array[1,1,2:4]
array[1,1,-1]
array[1,1,c(-1,-2)]

array[1,2:5,2]
array[1,c(2,3,4,5),2]
array[1,c(2,5),2]

array[1,c(2,5),2:3]
array[,c(2,5),2:3]

Data Structures (R-Objects)

Data Structures (R-Objects)

BONUS - Data Structures - Factor

• Factors are a special variable type for storing categorical variables.

• They sometimes behave like strings, and sometimes like integers.

gender = c(male", "female", "male", "male", "female")
gender
class(gender)
str(gender)
gender[2]


gender_factor <- factor(c("male", "female", "male", "male", "female"))
gender_factor
class(gender_factor)
str(gender_factor)
gender_factor[2]

Data Structures - Data Frame

• Each element is of the same length, like a matrix.
• A column can have different types.
• BUT, all the elements within a column are the same type.

Data Structures - Data Frame

person=c("Peter","Lois","Meg","Chris","Stewie")
age=c(42,40,17,14,1)
sex=factor(c("M","F","F","M","M"))
married=c(TRUE,TRUE,FALSE,FALSE,FALSE)

den <- matrix(c(person,married),5,2)
den <- matrix(c(age,married),5,2)
den <- matrix(c(person,age),5,2)
den <- matrix(c(person,sex),5,2)

no need to Combine

df <- data.frame(person,married)
df

class(df)
dim(df)
length(df)
str(df)
df$
df$person
as.character(df$person)

df <- data.frame(person,age,sex,married,stringsAsFactors=FALSE)
df
str(df)

Data Structures - Data Frame

person="Brian"
age=7
sex=factor("M")
married=FALSE

new_record_row <- data.frame(person,age,sex,married)
new_df <- rbind(df,newrecord) # Combine R Objects by Rows


surname=c("Yilmaz","Zeki","Sahin","Caliskan","Uslu","Guzel")
new_record_col <- data.frame(surname, stringsAsFactors=FALSE)
new_df2 <- cbind(new_df, new_record_col) # Combine R Objects by Columns

new_df2
new_df2[c(5,6),]
new_df2[c(5,6),] <- new_df2[c(6,5),]

new_df2[5]

a <- 9:14
a
a[2]
a[2,]
b <- matrix(a,2,3)
b
b[2]
b[2,]

Data Structures - Data Frame

new_df2
new_df2[1]
length(new_df2[1])
dim(new_df2[1])


new_df2[[1]]
length(new_df2[[1]])
dim(new_df2[[1]])

new_df2[[1]][2]
new_df2[[1]][2:5]

new_df2$person[2]
new_df2$person[2:5]

new_df2
new_df2[2:3,1:5]
new_df2[2:3,]

new_df2[2,1]
new_df2[2:2,1:1]

attributes(new_df2)

Data Structures - List

• Lists are like atomic vectors because they group data into a one-dimensional set.
• Lists are like data frame because they can group different types of data.
• BUT, the length of elements is NOT important.

Data Structures - List

matrix <- matrix(data=1:4,nrow=2,ncol=2)
vector <- c(T,F,T,T)
var <- "hello"
data_frame <- new_df2

list  <- list(matrix,vector,var,data_frame)
class(list)
str(list)
dim(list)
length(list)

R Language - Part 3

• Read

• Write

• Plot

Read

library(help="datasets")

list.files("/Users/emirtoker/Desktop/Dersler/Memurluk/Software_Tools_for_Earth_&_Environmental_Science/Software_Tools_R_Github/Presentation")

file.choose()

read.table(file = "18397_Cekmekoy_Omerli_15dk.txt")

read.table(file = "18397_Cekmekoy_Omerli_15dk.txt", 
          header=TRUE, sep=";")
          
read.table(file = "18397_Cekmekoy_Omerli_15dk.txt", 
          header=TRUE, sep=";", na.strings="-9999")

mydata_txt <- read.table(file = "18397_Cekmekoy_Omerli_15dk.txt",
                        header=TRUE, 
                        sep=";",
                        na.strings="-9999")
              
str(mydata_txt)

mydata_csv <- read.csv(file="18397_Cekmekoy_Omerli.csv",
                      header=TRUE,
                      na.strings="-9999")
                      
str(mydata_csv)

Read and Write

url <- "https://web.itu.edu.tr/tokerem/18397_Cekmekoy_Omerli_15dk.txt"
urldata_txt <- read.table(url,
                          header=TRUE, 
                          sep=";",
                          na.strings="-9999")

Write .TXT and .CSV

write.table(x=urldata_txt,file="somenewfile.txt")

write.table(x=urldata_txt,file="somenewfile.txt",
           sep=";",na="-9999",quote=FALSE,row.names=FALSE)

new_df2
write.table(x=new_df2,file="dffile.txt",
            sep=";",na="-9999",quote=FALSE,row.names=FALSE)
            
write.table(x=new_df2,file="dffile.csv",
            sep=";",na="-9999",quote=FALSE,row.names=FALSE)

Plot

foo <- c(1.1,2,3.5,3.9,4.2)
bar <- c(2,2.2,-1.3,0,0.2)
plot(foo,bar)

• type the supplied coordinates (for example, as stand-alone points or joined by lines or both dots and lines).
• main, xlab, ylab Options to include plot title, the horizontal axis label, and the vertical axis label, respectively.
• col Color (or colors) to use for plotting points and lines.
• lty Stands for line type. (for example, solid, dotted, or dashed).
• lwd This controls the thickness of plotted lines.
• xlim, ylim limits for the horizontal range and vertical range (respectively)

Plot

plot(foo,bar)
plot(foo,bar,type="l")
plot(foo,bar,type="b",main="My lovely plot",xlab="x axis label", ylab="location y")
plot(foo,bar,type="b",main="My lovely plot",xlab="",ylab="",col="red")

x <- 1:20
y <- c(-1.49,3.37,2.59,-2.78,-3.94,-0.92,6.43,8.51,3.41,-8.23,
-12.01,-6.58,2.87,14.12,9.63,-4.58,-14.78,-11.67,1.17,15.62)
plot(x,y,type="n",main="")
abline(h=c(-5,5),col="red",lty=2,lwd=2)
segments(x0=c(5,15),y0=c(-5,-5),x1=c(5,15),y1=c(5,5),col="red",lty=3,
lwd=2)
points(x[y>=5],y[y>=5],pch=4,col="darkmagenta",cex=2)
points(x[y<=-5],y[y<=-5],pch=3,col="darkgreen",cex=2)
points(x[(x>=5&x<=15)&(y>-5&y<5)],y[(x>=5&x<=15)&(y>-5&y<5)],pch=19,
col="blue")
points(x[(x<5|x>15)&(y>-5&y<5)],y[(x<5|x>15)&(y>-5&y<5)])
lines(x,y,lty=4)
arrows(x0=8,y0=14,x1=11,y1=2.5)
text(x=8,y=15,labels="sweet spot")
legend("bottomleft",
legend=c("overall process","sweet","standard",
"too big","too small","sweet y range","sweet x range"),
pch=c(NA,19,1,4,3,NA,NA),lty=c(4,NA,NA,NA,NA,2,3),
col=c("black","blue","black","darkmagenta","darkgreen","red","red"),
lwd=c(1,NA,NA,NA,NA,2,2),pt.cex=c(NA,1,1,2,2,NA,NA))

Plot

mydata_txt <- read.table(file = "18397_Cekmekoy_Omerli_15dk.txt",
                        header=TRUE, 
                        sep=";",
                        na.strings="-9999")

mydata_txt

plot(mydata_txt$temp, type="l" )

Workshop - Midterm Project

• Open a new R notebook
• Go to course home page, (Midterm Project)
• Click Rmd and Open “Midterm_Project.Rmd”
• Copy all code and paste in your R notebook
• Same way, open “18397_Cekmekoy_Omerli_15dk.txt” and paste file in your project directory.
• Start to follow Instructions

Next Week (not next, the other one)